The paper investigates how AI code agents perform when the surrounding code is rewritten in a semantically equivalent way. Using a random variant sampler that applies control‑flow rewrites, dead‑code injection, and identifier renaming, the authors evaluate two agent scaffolds—mini‑SWE agent and OpenCode—backed by four frontier models across SWE‑bench datasets. Results show modest drops in resolve‑rate (up to 6.7 percentage points) with significant degradations in 6 of 16 configurations, and reveal that robustness varies across models and scaffolds, forming a jagged frontier.
By Hasan Najib Mahmud (Colorado State University), Shreya Gupta (Microsoft), Isha Chaudhary (University of Illinois Urbana-Champaign), Nathaniel Enis (Colorado State University), Ravi Mangal (Colorado State University), Gagandeep Singh (University of Illinois Urbana-Champaign), Corina Pasareanu (Carnegie Mellon University)
arXiv:2507. 11059v3 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset.
By Pavel Adamenko, Mikhail Ivanov, Aidar Valeev, Rodion Levichev, Pavel Zadorozhny, Ivan Lopatin, Dmitry Babaev, Alena Fenogenova, Valentin Malykh
E2E-SWE is a benchmark that tests large language models’ ability to create complete, functional software repositories from scratch. It includes 186 tasks across 11 programming languages, each requiring an agent to build an installable project based solely on a natural‑language specification and an empty workspace, while passing a hidden test suite. The benchmark was crafted by software engineers and LLMs, then refined through iterative verification by autonomous agents to ensure clarity and solvability.
By Hantian Ding, Chloe Bi, Jiacheng Zhu, John Yang, Matt Deitke, Pengcheng Yin, Zijian Wang, Rui Hou
arXiv:2608. 14863v1 Announce Type: cross Abstract: LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified.
By Yibo Yan, Huijuan Wang, Junzhou He, Yizhuo Liang, Shaoyu Wang, Huanchen Sun, Seo Jin Park
arXiv:2610.09079v1 Announce Type: cross
Abstract: Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete r...
By Haibo Jin, Peng Kuang, Xucheng Yu, Jerry Wang, Dehao Wu, Haohan Wang
arXiv:2607. 18161v1 Announce Type: cross Abstract: Coding agents are increasingly used to accelerate code generation in many downstream tasks, such as fixing bugs, building applications, and prototyping.
By Alex Mathai, Shobini Iyer, Aleksandr Nogikh, Petros Maniatis, Franjo Ivancic, Junfeng Yang, Baishakhi Ray
arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.
By Oussama Ben Sghaier, Hao Li, Bram Adams, Ahmed E. Hassan
The paper introduces CABRA, a synthetic benchmark that constructs coding tasks from scratch using call‑graph transformations and scales difficulty along four axes: function traversal, search, runtime resolution, and instruction following. Experiments with eight large language models and six coding agents on 6,840 CABRA tasks show that while LLM accuracy degrades with task size, agents maintain high performance by leveraging tools; larger tasks trigger more tool calls, and a separate study on SWE‑bench verifies that tool‑call counts better predict agent accuracy than lines of code edited. The authors argue that CABRA exposes weaknesses in LLMs that are hidden by tool use and highlights the importance of code‑understanding abilities beyond mere editing.
whyItMatters":"The study demonstrates that coding agents’ performance is more dependent on code‑understanding than on the amount of code edited, revealing a critical bottleneck for current coding agents."
By Nishant Balepur, Kiran Tomlinson, Tobias Schnabel
Schr"odinger's Repository (Schr"odingerRepo) is an evaluation framework that tests coding agents on dynamically instantiated repository representations to mitigate data leakage from static repository benchmarks. It transforms test repositories through four levels—problem statement reconstruction, namespace remapping, intra-file layout reordering, and functionality-preserving code rewriting—to obscure familiar cues while preserving executable behavior. Experiments on popular LLMs using SWE-bench Verified and SWE-QA show that removing these cues consistently degrades performance and increases interaction costs, mainly due to harder repository exploration and localization.
By Silin Chen, Yufei Yang, Xiaodong Gu, Yuling Shi, Chengcheng Wan, Haibing Guan
arXiv:2606. 12344v1 Announce Type: new Abstract: General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring.
By Mengyu Zheng, Kai Han, Boxun Li, Haiyang Xu, Yuchuan Tian, Wei He, Hang Zhou, Jianyuan Guo, Hailin Hu, Lin Ma, Chao Xu, Guohao Dai, Lixue Xia, Yunchao Wei, Yunhe Wang, Yu Wang
arXiv:2607. 03691v1 Announce Type: cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agentic scaffolding: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.
By Oussama Ben Sghaier, Hao Li, Bram Adams, Ahmed E. Hassan
TestJack is a scalable framework that audits large language model (LLM) coding agents by generating new tests for each trial, focusing on prompt requirements that may be violated. It keeps only the tests passed by the ground‑truth patch and re‑examines failures, providing replayable evidence for each confirmed error. In experiments across six model backends and five benchmarks, TestJack found that 34.4% of trials deemed correct actually violated task requirements, reducing the overall resolution rate from 50.6% to 33.2%.
By Shuangjie Yao, Hao Wang, Koushik Sen, Simin Chen, Baishakhi Ray, Dawn Song